The identification of the protein fold class is a challenging problem in structural biology. Recent computational methods for fold prediction leverage deep learning techniques to extract protein fold-representative embeddings mainly using evolutionary information in the form of multiple sequence alignment (MSA) as input source. In contrast, protein language models (LM) have reshaped the field thanks to their ability to learn efficient protein representations (protein-LM embeddings) from purely sequential information in a self-supervised manner. In this paper, we analyze a framework for protein fold prediction using pre-trained protein-LM embeddings as input to several fine-tuning neural network models, which are supervisedly trained with fold labels. In particular, we compare the performance of six protein-LM embeddings: the long short-term memory-based UniRep and SeqVec, and the transformer-based ESM-1b, ESM-MSA, ProtBERT and ProtT5; as well as three neural networks: Multi-Layer Perceptron, ResCNN-BGRU (RBG) and Light-Attention (LAT). We separately evaluated the pairwise fold recognition (PFR) and direct fold classification (DFC) tasks on well-known benchmark datasets. The results indicate that the combination of transformer-based embeddings, particularly those obtained at amino acid level, with the RBG and LAT fine-tuning models performs remarkably well in both tasks. To further increase prediction accuracy, we propose several ensemble strategies for PFR and DFC, which provide a significant performance boost over the current state-of-the-art results. All this suggests that moving from traditional protein representations to protein-LM embeddings is a very promising approach to protein fold-related tasks.

MOTIVATION: Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. RESULTS: We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining. AVAILABILITY AND IMPLEMENTATION: Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Current state-of-the-art deep learning approaches for protein fold recognition learn protein embeddings that improve prediction performance at the fold level. However, there still exists aperformance gap at the fold level and the (relatively easier) family level, suggesting that it might be possible to learn an embedding space that better represents the protein folds. RESULTS: In this paper, we propose the FoldHSphere method to learn a better fold embedding space through a two-stage training procedure. We first obtain prototype vectors for each fold class that are maximally separated in hyperspherical space. We then train a neural network by minimizing the angular large margin cosine loss to learn protein embeddings clustered around the corresponding hyperspherical fold prototypes. Our network architectures, ResCNN-GRU and ResCNN-BGRU, process the input protein sequences by applying several residual-convolutional blocks followed by a gated recurrent unit-based recurrent layer. Evaluation results on the LINDAHL dataset indicate that the use of our hyperspherical embeddings effectively bridges the performance gap at the family and fold levels. Furthermore, our FoldHSpherePro ensemble method yields an accuracy of 81.3% at the fold level, outperforming all the state-of-the-art methods. CONCLUSIONS: Our methodology is efficient in learning discriminative and fold-representative embeddings for the protein domains. The proposed hyperspherical embeddings are effective at identifying the protein fold class by pairwise comparison, even when amino acid sequence similarities are low.

The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the protein fold recognition task. Our neural network model combines 1D-convolutional layers with gated recurrent unit (GRU) layers. The GRU cells, as recurrent layers, cope with the processing issues associated to the highly variable protein sequence lengths and so extract a fold-related embedding of fixed size for each protein domain. These embeddings are then used to perform the pairwise fold recognition task, which is based on transferring the fold type of the most similar template structure. We compare our model with several template-based and deep learning-based methods from the state-of-the-art. The evaluation results over the well-known LINDAHL and SCOP_TEST sets, along with a proposed LINDAHL test set updated to SCOP 1.75, show that our embeddings perform significantly better than these methods, specially at the fold level. Supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2020.3012732, source code and trained models are available at http://sigmat.ugr.es/~amelia/CNN-GRU-RF+/.

The importance of packet-based speech transmissions has grown since it offers cheaper and efficient communications. However, frame erasures are a common hurdle in these networks and concealment techniques are necessary to ensure a minimum quality of service. In this paper, we propose a mitigation technique focused on the reconstruction of the linear prediction coding (LPC) coefficients and the excitation signal of the lost frame by using a replacement technique. These replacements are obtained by means of a minimum mean square error estimation based on a source model of the speech parameters (LPC coefficients and the excitation signal). As this approach critically relies on the quantization and representation of the excitation signal, we explore the Haar wavelet transform as a novel approach to represent the excitation signal for error mitigation. Thus, this paper describes how optimal codebook and estimates can be computed in a Haar transformed domain. As a result, the excitation signal of a frame can be decomposed in several partitions where each one is independently reconstructed. Objective and subjective tests are conducted in order to assess the quality of the concealed speech signal resulting from our proposal. Both evaluations confirm noticeable improvements over the default mitigation method included in the two tested standard codecs, adaptive multirate, and Internet low bitrate codec.

The purpose of signal extrapolation is to estimate unknown signal parts from known samples. This task is especially important for error concealment in image and video communication. For obtaining a high quality reconstruction, assumptions have to be made about the underlying signal in order to solve this underdetermined problem. Among existent reconstruction algorithms, frequency selective extrapolation (FSE) achieves high performance by assuming that image signals can be sparsely represented in the frequency domain. However, FSE does not take into account the low-pass behaviour of natural images. In this paper, we propose a modified FSE that takes this prior knowledge into account for the modelling, yielding significant PSNR gains.

Many image error concealment (EC) algorithms can be employed to reconstruct a lost region by dividing it into a set of (sub)blocks which are estimated from a set of known context pixels. In turn, these former pixels may have been previously obtained by estimation. In this situation, the order in which the lost region is recursively filled will clearly condition the resulting reconstruction. This paper proposes a novel filling order aimed to improve the performance of EC algorithms that are applied recursively over the lost area. It takes into account the reconstruction quality of the already concealed blocks in order to determine the filling order. Blocks surrounded by known pixels or by high quality reconstructions are prioritized. The proposed technique is applicable to a wide range of EC algorithms and achieves an improvement of up to 1dB (in PSNR) with negligible additional computational load.

This paper proposes a novel approach for multimedia signal reconstruction based on kernel density estimation (KDE). We make use of a vector formalism in which vectors consist of a first subvector containing a set of missing samples and a second one containing a set of available context samples. The missing subvector is reconstructed by a minimum mean square error estimator which employs a probability density function (pdf) obtained by KDE. As in any kernel-based method, the main issue to deal with is the estimation of an appropriate kernel bandwidth. We propose an adaptive procedure for bandwidth estimation (BE) especially conceived for signal reconstruction. Thus, unlike general KDE or kernel-based regression, which try to obtain a general fit, the focus of this BE procedure is on the specific missing subvector. Also, in order to exploit local signal correlations, our BE proposal adopts a scaling approach in which the bandwidth is computed as the local covariance matrix scaled by two factors. These two scale factors are obtained by minimization of two different approximations to the reconstruction error. The resulting reconstruction methodology is tested on a spatial error concealment (EC) application in which intracoded images have been transmitted through an error prone channel. The experimental results show the superiority of the proposed approach over a wide range of existing EC techniques.

This paper proposes a methodology for the application of multivariate kernel density estimation (KDE) to MMSE-based image/video error concealment (EC). We show that the estimation of the kernel bandwidth matrix for EC must follow a criterion different from that of typical KDE problems. In particular, we propose a bandwidth built as the product of a structure matrix and a scale factor obtained with a minimum square error criterion. We show that our proposal can achieve average PSNR improvements larger than 1 dB with respect to other state-of-the-art techniques.

In this paper, we propose a novel sequential error concealment algorithm for video and images based on sparse linear prediction. Block-based coding schemes in packet loss environments are considered. Images are modelled by means of linear prediction, and missing macroblocks are sequentially reconstructed using the available groups of pixels. The optimal predictor coefficients are computed by applying a missing data regression imputation procedure with a sparsity constraint. Moreover, an efficient procedure for the computation of these coefficients based on an exponential approximation is also proposed. Both techniques provide high-quality reconstructions and outperform the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

In this paper we propose a novel spatial error concealment algorithm for video and images based on convex optimization. Block-based coding schemes in packet loss environment are considered. Missing macro blocks are sequentially reconstructed by filling them with a weighted set of templates extracted from the available neighbourhood. Moreover, a fast approximation of the optimization method is proposed. The technique produces high quality reconstructions that outperforms the state-of-the-art algorithms both in terms of PSNR and MS-SSIM.

This paper presents a noise estimation technique based on knowledge of pitch information for robust speech recognition. In the first stage the noise is estimated by means of extrapolating the noise from frames where speech is believed to be absent. These frames are detected with a proposed pitch based VAD (Voice Activity Detector). In the second stage the noise estimation is revised in voiced frames using harmonic tunnelling technique. The tunnelling noise estimation is used at high SNRs as an upper bound of the noise rather than a suitable estimation. A spectrogram MD (Missing Data) recognition system is chosen to evaluate the proposed noise estimation. The proposed system is compared in Aurora-2 with other similar techniques like cepstral SS (Spectral Subtraction).

In this paper, we propose two estimators for the autocorrelation sequence of a periodic signal in additive noise. Both estimators are formulated employing tables which contain all the possible products of sample pairs in a speech signal frame. The first estimator is based on a pitch-synchronous averaging. This estimator is statistically analyzed and we show that the signal-to-noise ratio (SNR) can be increased up to a factor equal to the number of available periods. The second estimator is similar to the former one but it avoids the use of those sample products more likely affected by noise. We prove that, under certain conditions, this estimator can remove the effect of an additive noise in a statistical sense. Both estimators are employed to extract mel frequency cepstral coefficients (MFCCs) as features for robust speech recognition. Although these estimators are initially conceived for voiced speech frames, we extend their application to unvoiced sounds in order to obtain a coherent feature extractor. The experimental results show the superiority of the proposed approach over other MFCC-based front-ends such as the higher-lag autocorrelation spectrum estimation (HASE), which also employs the idea of avoiding those autocorrelation coefficients more likely affected by noise.

In this paper, we present an improved quantization scheme for the redundancy data of a forward error correction (FEC) technique proposed for the transmission of code-excited linear prediction (CELP)-coded speech over erasure channels. The use of a FEC-based error protection scheme is motivated by the well-known fact that, after a frame erasure, the previous excitation is not available and a desynchronization between the encoder and the decoder long-term prediction (LTP) filters appears, causing an additional distortion which is propagated to subsequent frames. LTP synchronization can be recovered by means of a single-pulse representation of the previous excitation. No additional delay is introduced by this technique which only requires a small transmission bandwidth increase. In this paper, we focus on the efficient encoding of this pulse. Thus, an optimization procedure, which takes into account the overall synthesis error, is proposed in order to provide better pulse-position and pulse-amplitude quantization codebooks. Moreover, by extending the previous procedure, an efficient joint position-amplitude quantization can be obtained. Objective quality tests applied to our proposal show that, by means of the proposed codebooks, the number of bits required to represent the resynchronization pulse is effectively reduced. In addition, a discontinuous transmission mechanism is derived from the cost functional used during joint position-amplitude quantization, further reducing the bit-rate.

The widely used code-excited linear prediction (CELP) paradigm relies on a strong interframe dependency which renders CELP-based codecs vulnerable to packet loss. The use of long-term prediction (LTP) or adaptive codebooks (ACB) is the main source of interframe dependency in these codecs, since they employ the excitation from previous frames. After a frame erasure, previous excitation is unavailable and a desynchronization between the encoder and the decoder appears, causing an additional distortion which is propagated to the subsequent frames. In this paper, we propose a novel media-specific Forward Error Correction (FEC) technique which retrieves LTP-resynchronization with no additional delay at the cost of a very small bit of overhead. In particular, the proposed FEC code contains a multipulse signal which replaces the excitation of the previous frame (i.e., ACB memory) when this has been lost. This multipulse description of the previous excitation is optimized to minimize the perceptual error between the synthesized speech signal and the original one. To this end, we develop a multipulse formulation which includes the additional CELP processing and, in addition, can cope with the presence of advanced LTP filters and the usual subframe segmentation applied in modern codecs. Finally, a quantization scheme is proposed to encode pulse parameters. Objective and subjective quality tests applied to our proposal show that the propagation error due to LTP filter can practically be removed with a very little bandwidth increase.

This paper focuses on the application of a previously proposed interleaving, derived from the Ramsey convolutional class, in a voice streaming context with bursty packet losses. This kind of interleaving has already shown significant improvements in a distributed speech recognition context, in comparison with the widely used minimum latency block interleavers (MLBI). Here, the effectiveness of these interleavers is evaluated with an internet-oriented speech codec, such as iLBC. Since iLBC avoids error propagation due to lost frames and only uses the previously received frame to recover from those, Ramsey inter-leaving turns out especially suitable for this codec. In order to measure the performance of the system, the ITU PESQ algo-rithm is applied along with an intelligibility criterion based on the accuracy obtained through an automatic speech recognizer (ASR). In addition, an informal subjective test is carried out to corroborate the ASR scores. Results show that the proposed Ramsey-derived interleaving provides at least the same quality and better intelligibility than the MLBI ones when it is applied to iLBC codec. 1.

This paper presents a framework for the error concealment of multimedia signals transmitted over wireless and IP networks. This framework is based on two elements. First, the replacements for the erroneous channel outputs are obtained from a minimum mean square error (MMSE) estimation which takes into account both the source and all the available information from the transmission channel. Then, hidden Markov models (HMMs) provide the required stochastic modeling which allows the estimation. This modeling makes it possible a natural integration of the two types of information (source and available data from the channel). This framework is developed for both, wireless channels with errors at the bit level and packet channels degraded by packet loss. In the first case, a very high performance can be obtained even without the need of soft decision or any type of channel SNR estimation. In the second case, the lack of channel outputs during packet losses must be compensated either by enhancing the source model (increasing the model order) or by introducing media-specific FEC codes which may substitute the missing packets. This second option has yielded higher performance than the first one.

This paper proposes a method for the remote recognition of speech coded with the iLBC codec, which is employed by a number of VoIP systems. While the usual way of performing recognition of coded speech is to decode first the speech signal and use it as input to the recognition engine, our system directly converts the iLBC parameters into recognition features. The main advantage of this approach is to avoid any type of decoding post-processing which, although originally conceived to improve the speech perception, can be harmful for a recognition system. Our method ensures the compatibility between the speech spectra provided by the iLBC codec and those employed for cepstrum computation and introduces a robust and suitable packet loss concealment strategy. Our experimental results show that the proposed system achieves a performance better than that obtained from iLBC-decoded speech and similar to that of a distributed speech recognition system over a clean or degraded transmission channel.

In this work we present a complete set of techniques devoted to offer robustness against frame losses in distributed speech recognition over packet-switched networks. The proposed scheme is composed of tree techniques, two of them are applied at the sender and the last one in the recognizer itself. On one hand, a media-specific forward error correction (FEC) technique is used to allow the recovery of information within the bursts. On the other hand, a recognizer-based technique well known by its remarkable ability to reduce the effects of long consecutive frame losses during recognition, the weighted Viterbi algorithm (WVA), is used to handle the additional information introduced by FEC codes. Moreover, a double stream strategy whereby interleaving can be applied along with FEC codes without any delay increase, is also applied. The application of interleaving allows to reduce the perceived burst length at the receiver, further improving the recognition performance. As a result, the proposed scheme can provide an acceptable performance even under extremely adverse channel conditions.

This paper focuses on the application of the Ramsey class of interleavers to the problem of achieving robust distributed speech recognition in the presence of burst-like packet loss. Although block interleavers of minimal latency have been already proposed by other authors in order to cope with this problem, the Ramsey class offers a more powerful kind of interleavers. Taking into account the behavior of the error concealment techniques commonly used in these recognition systems, it is possible to design Ramsey interleavers which, in comparison with the block interleavers of minimal latency, can counteract longer bursts with the same latency and thus improve the robustness of the recognition system. The effectiveness of the proposed design criterion is verified by means of simulations and experimental comparisons

Abstract Interleaving has shown to be a useful technique to provide robustdistributed speech recognition over IP networks. This is due toits ability to disperse consecutive losses. However, this ability isrelated to the delay introduced by the interleaver. In this work,we propose a novel multi-flow block interleaver which exploitsthe presence of several streams and allows to reduce the involveddelay. Experimental results have shown that this interleaver ap-proximates the performance of end-to-end interleavers but with afraction of their delay. As disadvantage, this interleaver must beplaced in a common node where more than one flow are available. Index Terms : distributed speech recognition, IP networks, inter-leaving, active networks. 1. Introduction Since its beginning, Internet has been growing in size, incorporat-ing many new networks, as well as in functionality, adding newservices. As many other features have been integrated into In-ternet, such as mailing, instant messaging, telephony and so on,speech enabled services (SES) are also being incorporated. Theseservices provide ubiquitous speech recognition, allowing multipleusers to remotely access and share high performance recognitionengines.A very attractive approach to speech recognition over IP net-works is the distributed speech recognition (DSR) solution [1]. Asmany other services over Internet, it is based on a client-serverarchitecture. On one hand, a simple and low power client (

This paper presents a mixed recovery scheme for robust distributed speech recognition (DSR) implemented over a packet channel which suffers packet losses. The scheme combines media-specific forward error correction (FEC) and error concealment (EC). Media-specific FEC is applied at the client side, where FEC bits representing strongly quantized versions of the speech vectors are introduced. At the server side, the information provided by those FEC bits is used by the EC algorithm to improve the recognition performance. We investigate the adaptation of two different EC techniques, namely minimum mean square error (MMSE) estimation, which operates at the decoding stage, and weighted Viterbi recognition (WVR), where EC is applied at the recognition stage, in order to be used along with FEC. The experimental results show that a significant increase in recognition accuracy can be obtained with very little bandwidth increase, which may be null in practice, and a limited increase in latency, which in any case is not so critical for an application such as DSR

Network-based speech recognition (NSR) and distributed speech recognition (DSR) have been proposed as solutions to translate speech recognition technologies to mobile environments. NSR is the most straightforward solution since it does not require any modification in the mobile phone, however DSR offers higher robustness against codec compression and transmission channel degradation. This paper explores an alternative approach for remote speech recognition which combines the advantages of NSR and DSR. In this scheme, a standard speech codec is used for speech transmission but the recognition is performed from the received codec parameters. In particular, we focus on the effect of transmission channel errors, which can cause a more severe performance reduction on speech recognition than codec distortion. First, we show that an NSR solution can approach DSR through a reconstruction technique along with an adapted noise reduction technique originally proposed for acoustic noise. Then, these results are improved by working with recognition features directly extracted from the codec bitstream by means of parameter transcoding. Required modifications on current networks in order to access the bitstream are described. The network upgrading with the tandem free operation (TFO) protocol is an attractive solution. This upgrade not only offers an overall improvement on the end-to-end speech quality, but would also allow a recognition performance similar, and even higher in poor channel conditions, to that obtained by DSR when parameter transcoding along with the proposed mitigation techniques are applied

This paper proposes a new packet loss concealment technique based on the inclusion in each packet of a few FEC bits, representing data replicas, combined with a minimum mean square error estimation (MMSE). This technique is developed for an Aurora-2 distributed speech recognition system working over an IP network. In addition to the data representing the transmitted speech frames, each packet includes some FEC bits representing a strongly VQ-quantized version (replicas) of previous and subsequent frames. When a loss burst occurs, the lost frames can be reconstructed from the VQ replicas. In order to mitigate the degradation introduced by the coarse VQ quantization of the replicas, a model-based MMSE estimation is applied. The experimental results show that, under a strongly degraded channel, it is possible to obtain up to 83.31 % of word accuracy with only 4 FEC bits or 88.47 % with 8 FEC bits per packet, when the Aurora mitigation algorithm only obtains 76.98 %.

This work addresses the mitigation of channel errors by means of efficient minimum mean-square-error (MMSE) estimation. Although powerful model-based implementations have been recently proposed, the computational burden involved can make them impractical. We propose two new approaches that maintain a good level of performance with a low computational complexity. These approaches keep the simple structure and complexity of a raw MMSE estimation, although they enhance it with additional source a priori knowledge. The proposed techniques are built on a distributed speech recognition system. Different degrees of tradeoff between recognition performance and computational complexity are obtained.

Network-based speech recognition (NSR) using the conventional speech channel with the enhanced full rate (EFR) or the adaptive multi-rate (AMR) codec is a very attractive approach since no change to existing mobile phones is needed. However, NSR reveals a degrading performance due to both transmission channel errors and the speech encoding process in comparison with distributed speech recognition (DSR), where speech features are efficiently coded and transmitted on a data channel. We focus on the degradation of the speech features caused by channel errors in an NSR system and propose methods to improve the quality of these features. Applying these methods, it turns out that the performance of an NSR system based on EFR coding is comparable to that based on DSR.

A study of combined source and channel coding applied to LSP parameters in wideband speech coding is presented. The traditional approach to protect against channel errors is to increase the bit-rate for channel coding, decreasing the bit-rate of the source coding according the channel conditions. Joint source-channel coding is an alternative that provides a technique to mitigate channel errors without an increase of the bit-rate due to channel coding. This paper presents a study of channel optimized vector quantizer and channel optimized matrix quantizer applied to line spectral pairs (LSP) parameters in wideband speech coding. Gaussian and slow-fading Rayleigh channels are considered and GMSK (Gaussian minimum shift-keying) is used as the modulation technique. In addition, for comparison purposes, the performance of other schemes (split vector quantization, split matrix quantization and split multistage vector quantization) for quantizing the LSP parameters are evaluated.

Distributed speech recognition (DSR) has been recently proposed as an efficient way of translating automatic speech recognition technologies to mobile and IP network application. In this paper we propose a channel error mitigation technique with a low computational complexity that improves the mitigation technique proposed in the ETSI standard for DSR (ETSI-ES-201-108 v1.12) for bad channel conditions. We also study the influence of the vector quantization index assignment on the proposed mitigation technique and design a new index assignment that gets some improvement on the proposed technique.

In this paper, we develop a new mitigation technique for a distributed speech recognition system over IP. We have designed and tested several methods to improve the interpolation used in the Aurora DSR ETSI standard without any significant increase of computational cost at the decoder. These methods make use of the information contained in the data-source, because, in IP networks, unlike in cellular networks, no information is received during packet losses. When a packet loss occurs, the lost information can be reconstructed through estimations from the N nearest received packets. Due to the enormous amount of combinations from previous and next received speech vector sequences, we have developed a methodology that drastically reduces the amount of required estimations.

Distributed Speech Recognition involves the development of techniques to mitigate the degradations that the transmission channel introduces in the speech features. This work proposes an HMM framework from which different mitigation techniques oriented to bursty channels can be derived. In particular, two MMSE-based and a new Viterbi-based mitigation procedures are derived under this framework. Several implementation issues such as the channel SNR estimation or the application of hard decision on the received signal vectors are dealt with. Also, different boundary conditions suitable for the speech recognition application are studied for the different mitigation procedures. The experimental results show that the HMM-based techniques can effectively mitigate channel errors, even in very poor channel conditions.

This paper presents a novel technique that allows testing spoken dialogue systems by means of an automatic generation of conversations. The technique permits to easily test spoken dialogue systems under a variety of lab-simulated conditions, as it is easy to vary or change the utterance corpus used to check the performance of the system. The technique is based on the use of a module called user simulator whose purpose is to behave as real users when they interact with dialogue systems. The behaviour of the simulator is decided by means of diverse scenarios that represent the goals of the users. The simulator aim is to achieve the goals set in the scenarios during the interaction with the dialogue system. We have applied the technique to test a dialogue system developed in our lab. The test has been carried out considering different levels of white and babble noise as well as a VTS noise compensation technique. The results prove that the dialogue system performance is worse under the babble noise conditions. The VTS technique has been effective when dealing with noisy utterances and has lead to better experimental results, particularly for the white noise. The technique has permitted to detect problems in the dialogue strategies employed to handle confirmation turns and recognition errors, suggesting that these strategies must be improved.

Combined source and channel coding is a technique to mitigate channel errors without increasing the bit error rate. Channel optimized vector quantizer (COVQ) performs these objectives in the context of vector quantization. This paper presents a study of channel optimized matrix quantizer (COMQ) applied to quantize the line spectral pair (LSP) parameters as an extension of COVQ technique. Gaussian and slow-fading Rayleigh channels are considered and GMSK (Gaussian minimum shift-keying) is used as modulation technique. Several channel signal to noise ratio (CSNR) are considered to measure the performance of this system. In addition, for comparison purposes, the performance of other schemes for quantizing the LSP parameters are computed.

Recently, the first version of an ETSI standard for Distributed Speech Recognition has been proposed. The main benefit of this approach is the possibility of maintaining a high recognition performance when accessing remote information systems. The use of a digital channel for transmission of the encoded speech parameters implies the introduction of several channel distortions. Our paper deals with the mitigation of such distortions. We study the application of MMSE estimation to this problem and propose a new MMSE procedure that obtains the probabilities needed for MMSE from a forward-backward algorithm. We show that MMSE estimation obtains better performance than the mitigation algorithm described in the ETSI standard under different channel conditions.

We present a study of a channel optimized matrix quantizer (COMQ) applied to quantize LSP (line spectral pair) parameters in a CELP (code excited linear prediction) coder. A modification of the DoD FS-1016 standard (Campbell et al., 1989) is used for this purpose. A Gaussian channel is considered as the channel through which information is sent and BPSK (binary phase shift-keying) is used as modulation technique. To measure the performance of this coder several channel signal to noise ratios are considered. Also, the performance of the FS-1016 standard coder is computed for comparison purposes.

The algorithms for the reduction of the number of features without degrading the performance of pattern recognition systems play an important role in real applications. A new algorithm for feature selection is proposed. This algorithm is based on the discriminative feature extraction (DFE) technique and has been applied to speech recognition. The experimental results show that the recognition systems accept important reductions of the number of features without a degradation of the performance. For the representation used in our experiments, the recognition error-rate is not significantly increased when the number of components in the feature vector is reduced from 42 to 20.

This work presents the STACC, Sistema Telef onico Autom atico de Consulta de Calificaciones (Automatic Telephone System for Consulting Marks). This system has been developed at our laboratory during 1996 and implements a service through telephone line that allows the students to consult by speech their marks after the exams by means of a simple phone call. This experience provided us an interesting point of view about the problems of real applications of speech technology. In this work we describe the system and some statistics about the use of STACC by the students are presented.

Proposes a transform trellis coded quantization (TTCQ) scheme suitable for low-delay speech coding. This scheme is based on a general transform domain formulation for small frame sizes and the trellis coded quantization technique previously proposed where Ungerboeck's amplitude modulation trellises and set partitioning ideas are used for source coding. Using the discrete cosine transform the authors apply this technique to the coding of 7 kHz wideband speech, a matter of interest due to ISDN based applications. A 32 kbps low-delay coder is developed, simulation results showing a very high speech quality comparable to that obtained by the G722 standard at 64 kbps.>